General. All our models follow the general structure proposed by Hellendoorn et al. [1]}. The architecture consists of an input module (for mapping tokens to vectors), a central encoding model and localization and repair head. For constructing our baselines, we change the central encoding model. The remaining structure remains the same (if not specified otherwise). For the input module, we use a BPE subtoken encoder with a vocabulary of 10k subtokens and embed each token by averaging its subtoken representation.
